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Real-World AI Governance Center

Responsible AI for High-Stakes Human Systems

AI is already making and shaping decisions inside child welfare, public benefits, casework, and other systems where the people affected have the least power to contest an error. This Center treats that as a governance problem: not "is the model good?" but "does this whole sociotechnical system (model, people, records, pressures) catch its errors faster than it spreads them?"

Five sections, one discipline: concepts in the Field Guide, documented histories in the Domain Atlas, levers in the Practice Library, stress testing in the PAN Lab, and every empirical claim ledgered in the Evidence Registry. Oversight puts that same discipline into play, a story-driven governance game built on the PAN Lab engine.

The Center

The Real-World AI Governance Center

Five evidence-disciplined sections, one rule: nothing is claimed without support. Start anywhere.

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Evidence

The evidence discipline, in short

Every empirical claim on this site is a ledger entry mapped to sources synced from the PAN reference library, never added by hand. Conceptual framing is labeled as framing; each PAN Lab model organization is calibrated to a documented real-world deployment from the cited evidence; and statements still awaiting a source carry a visible "citation pending" badge rather than quiet confidence.

The full registry, including what's pending, is public at Evidence Registry.

Every claim here is ledgered and every pattern names what can backfire. If your organization is navigating one of these systems, the next step is a conversation about its actual shape.

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